code: def test_loss_and_predict(self):
      s = 256
      img_meta = {
          "img_shape": (s, s, 3),
          "pad_shape": (s, s, 3),
          "scale_factor": 1,
      }
      rpn_head = EmbeddingRPNHead(
          num_proposals=100, proposal_feature_channel=256
      )
  
      feats = [
          torch.rand(2, 1, s // (2 ** (i + 2)), s // (2 ** (i + 2)))
          for i in range(5)
      ]
  
      data_sample = DetDataSample()
      data_sample.set_metainfo(img_meta)
  
      # test predict
      result_list = rpn_head.predict(feats, [data_sample])
      self.assertTrue(isinstance(result_list, list))
      self.assertTrue(isinstance(result_list[0], InstanceData))
  
      # test loss_and_predict
      result_list = rpn_head.loss_and_predict(feats, [data_sample])
      self.assertTrue(isinstance(result_list, tuple))
      self.assertTrue(isinstance(result_list[0], dict))
      self.assertEqual(len(result_list[0]), 0)
      self.assertTrue(isinstance(result_list[1], list))
      self.assertTrue(isinstance(result_list[1][0], InstanceData))
  
      # test loss
      with pytest.raises(NotImplementedError):
          rpn_head.loss(feats, [data_sample])
  
file_path: mmdetection/tests/test_models/test_dense_heads/test_embedding_rpn_head.py
imports: from unittest import TestCase
  import pytest
  import torch
  from mmengine.structures import InstanceData
  from mmdet.models.dense_heads import EmbeddingRPNHead
  from mmdet.structures import DetDataSample
label_desc: Use image node_id 2 for calling the TestEmbeddingRPNHead obj's underlying member method code with example usage: obj.test_loss_and_predict() without return types